We present a new scale-invariant cost function for non-orthogonal joint-diagonalization of a set of symmetric matrices with application to Independent Component Analysis (ICA). We derive two gradient minimization schemes to minimize this cost function. We also consider their performance in the context of an ICA algorithm based on non-orthogonal joint diagonalization
International audienceIn this letter, a new algorithm for joint diagonalization of a set of matrices...
Abstract. This paper derives a new algorithm that performs independent component analysis (ICA) by o...
International audienceWe consider the classical problem of approximate joint diagonalization of matr...
In this thesis, employing the theory of matrix Lie groups, we develop gradient based flows for the p...
In this thesis, employing the theory of matrix Lie groups, we develop gradient based flows for the p...
ISR develops, applies and teaches advanced methodologies of design and analysis to solve complex, hi...
Independent Component Analysis is a popular statistical method for separating a multivariate signal ...
The Joint Diagonalization of a set of matrices by Congruence (JDC) appears in a number of signal pro...
5 pagesInternational audienceApproximate Joint Diagonalization (AJD) of a set of symmetric matrices ...
A new efficient algorithm is presented for joint diagonalization of several matrices. The algorithm ...
International audienceIn this paper, we focus on the Joint Diagonalization by Congruence (JDC) decom...
International audienceA new joint diagonalization by congruence algorithm is presented, which allows...
In this paper, we study the problem of non-orthogonal joint diagonalisation of a set of real symmetr...
Joint diagonalization of several correlation matrices is a powerful tool for blind signal separation...
Several blind source separation algorithms obtain a separating matrix by computing the congruence tr...
International audienceIn this letter, a new algorithm for joint diagonalization of a set of matrices...
Abstract. This paper derives a new algorithm that performs independent component analysis (ICA) by o...
International audienceWe consider the classical problem of approximate joint diagonalization of matr...
In this thesis, employing the theory of matrix Lie groups, we develop gradient based flows for the p...
In this thesis, employing the theory of matrix Lie groups, we develop gradient based flows for the p...
ISR develops, applies and teaches advanced methodologies of design and analysis to solve complex, hi...
Independent Component Analysis is a popular statistical method for separating a multivariate signal ...
The Joint Diagonalization of a set of matrices by Congruence (JDC) appears in a number of signal pro...
5 pagesInternational audienceApproximate Joint Diagonalization (AJD) of a set of symmetric matrices ...
A new efficient algorithm is presented for joint diagonalization of several matrices. The algorithm ...
International audienceIn this paper, we focus on the Joint Diagonalization by Congruence (JDC) decom...
International audienceA new joint diagonalization by congruence algorithm is presented, which allows...
In this paper, we study the problem of non-orthogonal joint diagonalisation of a set of real symmetr...
Joint diagonalization of several correlation matrices is a powerful tool for blind signal separation...
Several blind source separation algorithms obtain a separating matrix by computing the congruence tr...
International audienceIn this letter, a new algorithm for joint diagonalization of a set of matrices...
Abstract. This paper derives a new algorithm that performs independent component analysis (ICA) by o...
International audienceWe consider the classical problem of approximate joint diagonalization of matr...